Production of iron ore pellets in the iron and steel industries usually requires the stages of ore concentration and agglomeration of the iron-ore concentrates. The iron ore pelletization process consists of two key stages in which the iron ore fines are fed along with a mixture of binder (for example, bentonite), fuel (coal or coke) and flux (for example, limestone), and moisture to balling devices such as a rotating drum or disc to produce wet or green pellets. These wet pellets are loaded onto the strand of a moving grate to form a granular packed bed at the feed end of the induration furnace. Induration is essentially a heat treatment process in which the wet pellets are exposed to high temperatures and then cooled in order to impart the necessary mechanical and chemical properties and obtain the fired pellets.
A straight-grate induration furnace is used for the induration process of the wet pellets through thermal processing at elevated temperatures. As the strand of the moving grate moves from the feed end to the discharge end, the granular packed bed is subjected to increasingly hot process gas to dry and fire the pellets, and then ambient air is passed to cool the pellets. During the induration process several complex phenomena occur such as drying, hardening (or cooking), melting, and cooling of pellets, coke combustion, magnetite oxidation, and limestone calcination.
Typically, the straight-grate induration furnace comprises seven zones including an updraft drying (UDD) zone, a downdraft drying (DDD) zone, a preheating (PH) zone, a firing (F) or ignition (IGN) zone, an after-firing (AF) zone, a first phase cooling (CZ1) zone and a second phase cooling (CZ2) zone. Inside the furnace the gas/air streams flow through the porous moving bed vertically in cross-current direction with respect to the bed movement from the feed end to the discharge end. Multivariable interactions of the physico-chemical processes on the moving grate of the furnace make the process highly interactive and thus complex to control.
The quality of the iron ore pellets formed from the induration process is defined by the strength they achieve during this thermal processing and, has a direct relationship with the time-temperature history the wet pellets are exposed to inside the furnace. Due to the lack of any means to directly measure the granular packed bed temperature profile inside the furnace, the operation is controlled indirectly based on the maximum temperature of the off-gas exiting the bed below the strand of the moving grate. However, monitoring the off-gas temperature does not reveal the complete thermal picture of the granular packed bed. Also, the fired iron ore pellets discharged from the furnace have to be taken to a laboratory for off-line testing, in the absence of any real-time means to measure their strength. This information, however, can be obtained every 2 hr or 4 hr intervals only because of the time required for sample collection and testing in the laboratory apparatus.
Therefore, there is a need for a real-time optimization system which is based on reliable process models to detect and modify the process parameters to increase the process efficiency in terms of increasing the level of production, reducing the operating costs, improving the product quality control and reducing the energy and fuel consumption.
Several attempts have been made to automate process control and provide real-time optimization, some of these disclosures are listed in the prior art below:
U.S. Pat. No. 6,513,024 discloses a self-optimizing method and an article thereof for rapidly improving or optimizing performance of an object by carrying out several automatic experimental cycles on selected control variables as per computer-designed test matrices. The article comprises a computer readable program code means for performing a plurality of computerized automatic experimental cycles on the optimizable object relative to a plurality of control variables, wherein the computer readable program code means performs the steps of: computer-planning a designed experiment for each of the cycles, computer-executing each experimental cycle to obtain test results, computer-analyzing the test results for optimizing the performance of the object, computer-coding for storage in a readable form, and computer-storing.
US Publication No. 2002013664 discloses a system and method for control and monitoring of rotating equipment. The disclosure in US2002013664 provides a computer-implemented method for monitoring a mechanical component using a neural network or weighted distance classifier, wherein the method references a predetermined set of candidate data features for a sensor measuring an operational attribute of the component and derives a subset of those features which are then used in real-time to determine parameter variables. The database is updated in real-time when an anomalous measurement is encountered.
US Publication No. 2009193936 discloses a method and system for on-line quality prediction and control in an oxygen furnace. The system in accordance with US2009193936 comprises a database configured to store historical data associated with a first turndown quality, a prediction module coupled to the database, including a computer-implemented model of the furnace based on support vector regression which is a statistical technique to produce a prediction of first turndown quality and further configured to receive the historical data, and a run-to-run control module coupled to the prediction module and configured to apply the model to the historical data to obtain a prediction of first turndown quality and compare the prediction to an actual measurement to adjust a control recipe for the oxygen furnace.
US Publication No. 2010219567 discloses an apparatus and a method thereof for controlling a process line such as continuous annealing line or plating line where steel material is continuously processed. The method as disclosed in US2010219567 comprises measuring quality of the steel material at a position preceding the heating process and a position succeeding the cooling process, checking the measurement results to determine whether the material is acceptable or not on the basis of a determination criteria, recording in a database, correcting process conditions including heating or cooling temperatures, and conveyance speed of the steel material.
PCT Publication No. WO201092430 discloses a method and a system for optimization of parameters for a recovery boiler. The system of WO201092430 discloses a process model component having a process model describing relationship between various process variables for at least one unit, a parameter estimation component to estimate at least one unit parameter, a controller component to control a second unit based on the estimated parameter, wherein the process model is based completely or partially on a first principle mathematical model and the parameter estimation component uses online measurements made along the various units of the boiler, computation of process variables using the process model, use of laboratory data to estimate the boiler parameters that are not directly measurable.
A technical paper published by Dominique Pomerleau et. al., in the 11th Mediterranean Conference on Control and Automation, titled “Optimization of a simulated iron-oxide pellets induration furnace”, suggests real-time optimization of the induration furnace based on reliable process models using IMC-optimization algorithm, a static nonlinear optimization algorithm, the model providing process parameters like gas stream temperature profile, energy balance and pressure drop in the gas streams.
The present invention discloses one such optimization system, particularly for an induration furnace used for the production of iron ore pellets, which uses a model-based component for predicting in real-time, process and pellet quality parameters that cannot be measured on-line and simulation and optimization algorithms, which are used to implement real-time optimization of the operation of the induration furnace, to optimize the productivity without affecting the product quality.